Datasets:
Tasks:
Text Classification
Formats:
parquet
Languages:
English
Size:
1K - 10K
Tags:
structured-decisions
calibration
probabilistic-classification
system-one
workflow-evaluation
Synthetic
License:
| license: apache-2.0 | |
| language: | |
| - en | |
| pretty_name: Typed Decisions | |
| size_categories: | |
| - n<1K | |
| task_categories: | |
| - text-classification | |
| tags: | |
| - structured-decisions | |
| - calibration | |
| - probabilistic-classification | |
| - system-one | |
| - workflow-evaluation | |
| - synthetic | |
| configs: | |
| - config_name: agent_trace_observability | |
| data_files: | |
| - split: train | |
| path: agent_trace_observability/train-*.parquet | |
| - split: test | |
| path: agent_trace_observability/test-*.parquet | |
| - config_name: customer_service | |
| data_files: | |
| - split: train | |
| path: customer_service/train-*.parquet | |
| - split: test | |
| path: customer_service/test-*.parquet | |
| - config_name: invoice_processing | |
| data_files: | |
| - split: train | |
| path: invoice_processing/train-*.parquet | |
| - split: test | |
| path: invoice_processing/test-*.parquet | |
| - config_name: security_incidents | |
| data_files: | |
| - split: train | |
| path: security_incidents/train-*.parquet | |
| - split: test | |
| path: security_incidents/test-*.parquet | |
| - config_name: all | |
| data_files: | |
| - split: train | |
| path: all/train-*.parquet | |
| - split: test | |
| path: all/test-*.parquet | |
| # Typed Decisions | |
| A benchmark for typed probabilistic decisions. A model gets one piece of | |
| unstructured state and answers five typed questions about it at once, and every | |
| answer is a probability distribution, not a single label. | |
| The schema follows the System One primitives (`noul`, `choice`, `score`) used by | |
| [TypeSafe AI](https://typesafe.ai), so a row replays against any API with that | |
| shape. The benchmark is independent: it is not affiliated with TypeSafe and does | |
| not reproduce their Jev model. | |
|  | |
| ## Leaderboard | |
| `test` split: 400 cases, 2,000 decisions. General models are scored zero-shot; | |
| they have never seen these workflows or their question schemas. | |
| | # | Model | Kind | Accuracy ↑ | KL from gold ↓ | Brier ↓ | ECE ↓ | p50 latency | Price / 1M input | | |
| |---|---|---|---|---|---|---|---|---| | |
| | 1 | **[meraGPT Decider 1](https://meragpt.com/models/state-decider-1?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=typed-decisions)** (`sd-1`) | general, zero-shot | **0.768** | **0.096** | **0.052** | 0.180 | **526 ms** | **$0.03** | | |
| | 2 | [Liquid AI d1](https://docs.liquid.ai/lfm/models/decision-models) (`d1:free`) | general, zero-shot | 0.742 | 0.475 | 0.155 | 0.124 | 525 ms | free tier | | |
| | 3 | TypeSafe Jev 1.13.0 | general, zero-shot | 0.727 | 1.442 | 0.148 | 0.144 | 710 ms | $0.042 | | |
| | 4 | [Featherless Simple Jev](https://simple-jev.featherless.ai) (`Qwen3.6-35B-A3B-classifier`) | general, zero-shot | 0.716 | 0.488 | 0.176 | – | – | free demo | | |
| | 5 | ModernBERT-base (149M) | specialist, fitted per workflow | 0.646 | 0.223 | 0.119 | 0.179 | 349 ms† | – | | |
| | 6 | MiniLM-L6 (22M) | specialist, fitted per workflow | 0.587 | 0.262 | 0.143 | 0.108 | 22 ms† | – | | |
| | 7 | [Jeff-Gemma4-E2B](https://huggingface.co/mstrasser/Jeff-Gemma4-E2B) | general, zero-shot, open weights | 0.561 | 0.403 | 0.219 | 0.188 | 2,272 ms† | open weights | | |
| | 8 | [Jeff-Qwen3.5-2B](https://huggingface.co/mstrasser/Jeff-Qwen3.5-2B) | general, zero-shot, open weights | 0.511 | 0.460 | 0.237 | 0.203 | 1,346 ms† | open weights | | |
| | 9 | [Jeff-Qwen3.5-0.8B](https://huggingface.co/mstrasser/Jeff-Qwen3.5-0.8B) | general, zero-shot, open weights | 0.483 | 0.679 | 0.313 | 0.251 | 662 ms† | open weights | | |
| | – | Prior (ignores the input) | reference | 0.470 | 0.347 | 0.189 | 0.088 | – | – | | |
| | – | Uniform (same probability on every option) | reference | 0.308 | 0.444 | 0.238 | 0.169 | – | – | | |
| Every row was scored by sending the whole case (state plus all five questions) in | |
| one request. Request shape matters: in a third-party run Jev's yes/no accuracy was | |
| 0.843 with one question per request and 0.788 alongside the others. | |
| Hosted rows are p50 end to end from a client, one request at a time. | |
| † Measured on the same machine as the model (M3 Max), not comparable with hosted latency. | |
| **[meraGPT Decider 1](https://meragpt.com/models/state-decider-1?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=typed-decisions) is state of | |
| the art on this benchmark.** It leads Jev on every question type (`noul` 0.840 vs | |
| 0.775, `choice` 0.733 vs 0.720, `score` 0.739 vs 0.696), its distributions sit far | |
| closer to the gold (KL 0.096 vs 1.442), it is faster end to end, and it costs less | |
| per token. It answers `POST /v1/systemone` at | |
| [meragpt.com](https://meragpt.com/docs/systemone?utm_source=huggingface&utm_medium=dataset_card&utm_campaign=typed-decisions), so the typesafe-sdk works | |
| against it by setting `TYPESAFE_BASE_URL`. | |
| To add a model, score it on `test` with the full distributions and open a | |
| discussion with the numbers and the mode (specialist or general) it used. | |
| ### Notes on the rows | |
| - **Liquid AI d1** was measured on 2026-09-30 through Liquid's API | |
| (`https://api.liquid.ai/v1/systemone`, `model: d1:free`), with the same client | |
| code as the Jev row: all 2,000 decisions, zero errors. It ties Decider 1 on | |
| `noul` (0.840) and `choice` (0.732 vs 0.733) and trails on `score` (0.677 vs | |
| 0.739), and it beats Jev on accuracy and KL. Liquid lists no per-token price | |
| yet, so the row shows the free tier. | |
| - **Jev 1.13.0** was measured on 2026-09-18 through TypeSafe's API (`jev-latest`, | |
| which reported `jev-1.13.0`): all 2,000 decisions, zero errors, $0.016 in total. | |
| Its accuracy is near the 0.735 ceiling, but it puts nearly all its probability | |
| on one answer, which is where the KL gap comes from. Its confidence is not badly | |
| calibrated (overconfidence +0.023); the gold is a three-sample spread it does not | |
| reproduce. | |
| - **Jeff** ([firelex/jeff](https://github.com/firelex/jeff), Apache-2.0) was scored | |
| on 2026-09-29 through the same client code as Jev, against Jeff's own | |
| `jeff-serve` (commit `2c1bfce`) with the calibration each checkpoint ships. All | |
| three clear the Prior on accuracy but not on KL. Jeff's own 83.1% comes from a | |
| different five-benchmark panel. If there is a better way to serve them, open a | |
| discussion and we will rescore. | |
| - **Specialists** use [Adaptive Classifier](https://github.com/codelion/adaptive-classifier) | |
| 0.2.0, one classifier per question on a frozen encoder, tuned on a held-out | |
| quarter of `train` (mean pooling, `max_length` 512, 30 epochs, | |
| `prototype_weight` 0.3). Each case is entered four times, split across labels in | |
| proportion to its gold, so the soft target survives hard-label training; that | |
| cut KL by a third. | |
| - **Prior** answers each question's `train` label frequencies for every case. It | |
| has the best ECE while knowing nothing, which is why KL and Brier are the | |
| columns to read, not ECE. | |
| ## Reading a score | |
| Gold is the mean of three samples from a teacher of roughly 4B-class capability, | |
| so a score measures agreement with that teacher, not correctness. A better model | |
| can score lower wherever the teacher is wrong (it missed a duplicate invoice whose | |
| ID matched an earlier one). | |
| | Reference | Accuracy | What it is | | |
| |---|---|---| | |
| | Prior | 0.470 | the floor: label frequencies, ignoring the input | | |
| | Perfect factor recovery | 0.704 | a model fitted to the latent factors that generated each case | | |
| | Teacher self-agreement | 0.735 | a fresh teacher sample against gold built from the others | | |
| Scores well above 0.735 mean a model is learning the teacher's quirks. Per-question | |
| ceilings vary from 0.560 (`agent_trace/urgency`) to 0.937 | |
| (`customer_service/category`), so read scores per question as well as on average. | |
| **Specialist and general numbers are not comparable.** A specialist is fitted on | |
| `train` for these four workflows and cannot answer anything else. A general model | |
| takes any question schema at request time and has never seen these. Say which | |
| mode you used; the gap between them is the price of generality, not a quality | |
| ranking. | |
| ## The data | |
| | Type | Answer | Shape | | |
| |---|---|---| | |
| | `noul` | yes/no | probability that the statement is true | | |
| | `choice` | one of N labels | distribution over labels, plus confidence | | |
| | `score` | an ordered rubric | distribution over levels, plus an expected score | | |
| Every option carries a written description in `criteria`, and the descriptions | |
| are part of the input. | |
| | Workflow | Decision | Train | Test | | |
| |---|---|---|---| | |
| | `agent_trace_observability` | Does an agent run need human review, and how urgently? | 300 | 100 | | |
| | `customer_service` | The right response and action for a customer thread and account. | 300 | 100 | | |
| | `invoice_processing` | Pay, hold or reject a vendor bill against its order and delivery. | 300 | 100 | | |
| | `security_incidents` | Close, investigate or contain a security alert, given machine history. | 300 | 100 | | |
| `state` and `questions` together are exactly the body of a `POST /v1/systemone` | |
| request. `gold` holds the full gold distributions, and flat | |
| `<question>__label` / `__probabilities` / `__score` / `__probability_true` columns | |
| hold the same answers for convenience. `factors` and `label_agreement` describe how | |
| the case was built and are not model input. | |
| ```python | |
| from datasets import load_dataset | |
| import json | |
| ds = load_dataset("LocalLLaMA/typed-decisions", "customer_service", split="test") | |
| row = ds[0] | |
| state, questions, gold = (json.loads(row[k]) for k in ("state", "questions", "gold")) | |
| print(gold["urgency"]["probabilities"]) # score against the full distribution | |
| ``` | |
| Report KL or log loss and Brier next to accuracy; calibration is the point. | |
| ## How it was built | |
| Each case starts from independently sampled latent factors (topic, tone, | |
| severity, discrepancy type and so on), which a model renders into free text where | |
| the artefact is textual and keeps structured where it is structured. A teacher | |
| labels each case three times at temperature 0.7, and the gold is the mean of those | |
| distributions, so it stays soft where a decision is genuinely ambiguous. Before | |
| release, an audit checks state diversity, label balance, and that the gold | |
| actually tracks the input. `train` comes from a separate run at a different seed; | |
| packaging refuses to build if any case id or state appears in both splits. | |
| The write-up behind the benchmark, including the two bugs it exposed in the | |
| classifier library: [Typed Decisions on Latent Node](https://latentnode.pages.dev/articles/typed-decisions.html). | |